GliaMorph: a modular image analysis toolkit to quantify Müller glial cell morphology.
Elisabeth C KuglerIsabel BravoXhuljana DurmishiStefania MarcottiSara BeqiriAlicia CarringtonBrian StramerPierre MattarRyan B MacDonaldPublished in: Development (Cambridge, England) (2023)
Cell morphology is crucial for all cell functions. This is particularly true for glial cells as they rely on complex shape to contact and support neurons. However, methods to quantify complex glial cell shape accurately and reproducibly are lacking. To address this, we developed the image analysis pipeline 'GliaMorph'. GliaMorph is a modular analysis toolkit developed to perform (1) image pre-processing, (2) semi-automatic region-of-interest selection, (3) apicobasal texture analysis, (4) glia segmentation, and (5) cell feature quantification. Müller glia (MG) have a stereotypic shape linked to their maturation and physiological status. Here, we characterized MG on three levels: (1) global image-level, (2) apicobasal texture, and (3) regional apicobasal vertical-to-horizontal alignment. Using GliaMorph, we quantified MG development on a global and single-cell level, showing increased feature elaboration and subcellular morphological rearrangement in the zebrafish retina. As proof of principle, we analysed expression changes in a mouse glaucoma model, identifying subcellular protein localization changes in MG. Together, these data demonstrate that GliaMorph enables an in-depth understanding of MG morphology in the developing and diseased retina.
Keyphrases
- single cell
- deep learning
- cell therapy
- rna seq
- machine learning
- neuropathic pain
- oxidative stress
- induced apoptosis
- poor prognosis
- magnetic resonance
- computed tomography
- spinal cord injury
- optical coherence tomography
- cell proliferation
- mesenchymal stem cells
- convolutional neural network
- big data
- endoplasmic reticulum stress
- amino acid